Higher level representation splay an important role in model management systems. The role is to make decision makers friendly represent their problem using the representations. In this research, we address higher level representations including five distinctivenesses: Objective, Node, Link, Topological Constraint including five components, and Decision, Therefore, it is developed a system called HLRNET that implements the building procedure of network models using structural knowledge and object data The paper particularly elaborates all components included in each of distinctiveness extracted from structural characteristics of a lot of telecommunication network models. Higher level representations represented with five destinctivenesses should be converted into base level representations which are employed for semantic representations of linear and integer programming problems in a knowledge-assisted optimization modeling system. The system is illustrated with an example of the local access network model.